# Object detection on aerial imagery using CenterNet

Source: [Object detection on aerial imagery using CenterNet](<https://www.sota2.com/research/paper/68fa66391e209131cea0eee4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:45:30.461Z

Authors: Dheeraj Reddy Pailla, Varghese Kollerathu, Sai Saketh Chennamsetty

Published: 2019-08-22T08:10:23.000Z

## Abstract

Detection and classification of objects in aerial imagery have several applications like urban planning, crop surveillance, and traffic surveillance. However, due to the lower resolution of the objects and the effect of noise in aerial images, extracting distinguishing features for the objects is a challenge. We evaluate CenterNet, a state of the art method for real-time 2D object detection, on the VisDrone2019 dataset. We evaluate the performance of the model with different backbone networks in conjunction with varying resolutions during training and testing.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.08244>)

[PDF](<https://arxiv.org/pdf/1908.08244.pdf>)